Monthly Archives: October 2016

Considering the success in of Gradient boosting machines in machine learning, one might think of the application of the same principle to elections. The rationale behind it being that voters may have changed their vote conditioned on the knowledge of the current power distribution is. Unconditional voting may lead to power distributions which may be…

If you want to find out, that you share quite a lot of confusion regarding the estimand concept with the RSS/PSI experts, I recommend listening to the audio recording of the RSS/PSI journal club on estimands from 2016-07-06 on YouTube [1]. Personally, I think that much of the confusion around the estimand concept (except not…

Gradient Boosting Machines as a family of methods have been the “talk of the town” in the Machine learning world for a while now, with the specific flavour of Gradient Boosting Trees has been regarded as “the best off-the-shelf” classifier in the world” (Breiman, 1986/1987, see Hastie et al. 2013) [1]. A wonderful review about…

A critical NATURE article about income inequality across academic career levels focused wage distribution in California, US [1]. I would like to see this also in the context of great poverty of students at the University of California, Los Angeles, USA, [2], but also poor students at German Universities [3]. After experiencing myself that I…